Overview

Brought to you by YData

Dataset statistics

Number of variables22
Number of observations899164
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory323.4 MiB
Average record size in memory377.2 B

Variable types

Categorical6
Numeric12
Text3
DateTime1

Alerts

RevLineCr has constant value "0" Constant
ApprovalFY is highly overall correlated with IsFranchise and 2 other fieldsHigh correlation
DisbursementGross is highly overall correlated with GrAppv and 2 other fieldsHigh correlation
GrAppv is highly overall correlated with DisbursementGross and 2 other fieldsHigh correlation
IsFranchise is highly overall correlated with ApprovalFYHigh correlation
RetainedJob is highly overall correlated with ApprovalFYHigh correlation
SBA_Appv is highly overall correlated with DisbursementGross and 2 other fieldsHigh correlation
Term is highly overall correlated with DisbursementGross and 2 other fieldsHigh correlation
UrbanRural is highly overall correlated with ApprovalFYHigh correlation
NoEmp is highly skewed (γ1 = 80.24824355) Skewed
CreateJob is highly skewed (γ1 = 36.99135473) Skewed
RetainedJob is highly skewed (γ1 = 36.85481184) Skewed
LoanNr_ChkDgt has unique values Unique
NAICS has 201948 (22.5%) zeros Zeros
CreateJob has 629248 (70.0%) zeros Zeros
RetainedJob has 440403 (49.0%) zeros Zeros
FranchiseCode has 208835 (23.2%) zeros Zeros

Reproduction

Analysis started2025-02-10 10:13:52.176689
Analysis finished2025-02-10 10:14:43.416760
Duration51.24 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

MIS_Status
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size42.9 MiB
1
741345 
0
157819 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters899164
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 741345
82.4%
0 157819
 
17.6%

Length

2025-02-10T11:14:43.478712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-10T11:14:43.522476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 741345
82.4%
0 157819
 
17.6%

Most occurring characters

ValueCountFrequency (%)
1 741345
82.4%
0 157819
 
17.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 899164
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 741345
82.4%
0 157819
 
17.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 899164
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 741345
82.4%
0 157819
 
17.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 899164
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 741345
82.4%
0 157819
 
17.6%

LoanNr_ChkDgt
Real number (ℝ)

Unique 

Distinct899164
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7726123 × 109
Minimum1.000014 × 109
Maximum9.996003 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2025-02-10T11:14:43.592810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.000014 × 109
5-th percentile1.3484572 × 109
Q12.5897575 × 109
median4.361439 × 109
Q36.9046265 × 109
95-th percentile9.1648039 × 109
Maximum9.996003 × 109
Range8.995989 × 109
Interquartile range (IQR)4.314869 × 109

Descriptive statistics

Standard deviation2.538175 × 109
Coefficient of variation (CV)0.53182091
Kurtosis-1.086499
Mean4.7726123 × 109
Median Absolute Deviation (MAD)2.0134 × 109
Skewness0.3647571
Sum4.2913612 × 1015
Variance6.4423325 × 1018
MonotonicityStrictly increasing
2025-02-10T11:14:43.685040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9996003010 1
 
< 0.1%
1000014003 1
 
< 0.1%
1000024006 1
 
< 0.1%
1000034009 1
 
< 0.1%
1000044001 1
 
< 0.1%
1000054004 1
 
< 0.1%
1000084002 1
 
< 0.1%
1000093009 1
 
< 0.1%
1000094005 1
 
< 0.1%
1000104006 1
 
< 0.1%
Other values (899154) 899154
> 99.9%
ValueCountFrequency (%)
1000014003 1
< 0.1%
1000024006 1
< 0.1%
1000034009 1
< 0.1%
1000044001 1
< 0.1%
1000054004 1
< 0.1%
1000084002 1
< 0.1%
1000093009 1
< 0.1%
1000094005 1
< 0.1%
1000104006 1
< 0.1%
1000124001 1
< 0.1%
ValueCountFrequency (%)
9996003010 1
< 0.1%
9995973006 1
< 0.1%
9995613003 1
< 0.1%
9995603000 1
< 0.1%
9995573004 1
< 0.1%
9995563001 1
< 0.1%
9995493004 1
< 0.1%
9995473009 1
< 0.1%
9995453003 1
< 0.1%
9995423005 1
< 0.1%

State
Text

Distinct51
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size43.7 MiB
2025-02-10T11:14:43.814460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1798328
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIN
2nd rowIN
3rd rowIN
4th rowOK
5th rowFL
ValueCountFrequency (%)
ca 130621
 
14.5%
tx 70462
 
7.8%
ny 57693
 
6.4%
fl 41213
 
4.6%
pa 35170
 
3.9%
oh 32622
 
3.6%
il 29669
 
3.3%
ma 25272
 
2.8%
mn 24374
 
2.7%
nj 24036
 
2.7%
Other values (41) 428032
47.6%
2025-02-10T11:14:43.977308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 306178
17.0%
C 184959
10.3%
N 181729
10.1%
M 132551
 
7.4%
T 125074
 
7.0%
I 119519
 
6.6%
O 94908
 
5.3%
L 88820
 
4.9%
X 70462
 
3.9%
Y 68255
 
3.8%
Other values (14) 425873
23.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1798328
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 306178
17.0%
C 184959
10.3%
N 181729
10.1%
M 132551
 
7.4%
T 125074
 
7.0%
I 119519
 
6.6%
O 94908
 
5.3%
L 88820
 
4.9%
X 70462
 
3.9%
Y 68255
 
3.8%
Other values (14) 425873
23.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1798328
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 306178
17.0%
C 184959
10.3%
N 181729
10.1%
M 132551
 
7.4%
T 125074
 
7.0%
I 119519
 
6.6%
O 94908
 
5.3%
L 88820
 
4.9%
X 70462
 
3.9%
Y 68255
 
3.8%
Other values (14) 425873
23.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1798328
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 306178
17.0%
C 184959
10.3%
N 181729
10.1%
M 132551
 
7.4%
T 125074
 
7.0%
I 119519
 
6.6%
O 94908
 
5.3%
L 88820
 
4.9%
X 70462
 
3.9%
Y 68255
 
3.8%
Other values (14) 425873
23.7%

Zip
Real number (ℝ)

Distinct33611
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53804.391
Minimum0
Maximum99999
Zeros283
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2025-02-10T11:14:44.058974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3838
Q127587
median55410
Q383704
95-th percentile95822
Maximum99999
Range99999
Interquartile range (IQR)56117

Descriptive statistics

Standard deviation31184.159
Coefficient of variation (CV)0.5795839
Kurtosis-1.3359893
Mean53804.391
Median Absolute Deviation (MAD)28206
Skewness-0.16816663
Sum4.8378972 × 1010
Variance9.7245178 × 108
MonotonicityNot monotonic
2025-02-10T11:14:44.146714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10001 933
 
0.1%
90015 926
 
0.1%
93401 806
 
0.1%
90010 733
 
0.1%
33166 671
 
0.1%
90021 666
 
0.1%
59601 640
 
0.1%
65804 599
 
0.1%
3801 581
 
0.1%
59101 578
 
0.1%
Other values (33601) 892031
99.2%
ValueCountFrequency (%)
0 283
< 0.1%
1 24
 
< 0.1%
2 11
 
< 0.1%
3 5
 
< 0.1%
4 5
 
< 0.1%
5 5
 
< 0.1%
6 4
 
< 0.1%
7 6
 
< 0.1%
8 15
 
< 0.1%
9 24
 
< 0.1%
ValueCountFrequency (%)
99999 209
< 0.1%
99950 3
 
< 0.1%
99929 15
 
< 0.1%
99928 1
 
< 0.1%
99926 1
 
< 0.1%
99925 4
 
< 0.1%
99923 1
 
< 0.1%
99921 13
 
< 0.1%
99919 2
 
< 0.1%
99918 1
 
< 0.1%

UrbanRural
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size42.9 MiB
1
470654 
0
323167 
2
105343 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters899164
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 470654
52.3%
0 323167
35.9%
2 105343
 
11.7%

Length

2025-02-10T11:14:44.220598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-10T11:14:44.264729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 470654
52.3%
0 323167
35.9%
2 105343
 
11.7%

Most occurring characters

ValueCountFrequency (%)
1 470654
52.3%
0 323167
35.9%
2 105343
 
11.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 899164
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 470654
52.3%
0 323167
35.9%
2 105343
 
11.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 899164
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 470654
52.3%
0 323167
35.9%
2 105343
 
11.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 899164
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 470654
52.3%
0 323167
35.9%
2 105343
 
11.7%

Bank
Text

Distinct5803
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size61.9 MiB
2025-02-10T11:14:44.420032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length30
Median length26
Mean length23.159879
Min length3

Characters and Unicode

Total characters20824529
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique923 ?
Unique (%)0.1%

Sample

1st rowFIFTH THIRD BANK
2nd row1ST SOURCE BANK
3rd rowGRANT COUNTY STATE BANK
4th row1ST NATL BK & TR CO OF BROKEN
5th rowFLORIDA BUS. DEVEL CORP
ValueCountFrequency (%)
bank 651608
18.5%
natl 318240
 
9.0%
assoc 306768
 
8.7%
of 142852
 
4.1%
national 125899
 
3.6%
america 100686
 
2.9%
association 84965
 
2.4%
fargo 63732
 
1.8%
wells 63650
 
1.8%
52264
 
1.5%
Other values (3603) 1608268
45.7%
2025-02-10T11:14:44.684586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 2762231
13.3%
2620014
12.6%
N 2105500
10.1%
S 1520499
 
7.3%
O 1336993
 
6.4%
T 1181841
 
5.7%
C 1134642
 
5.4%
I 1061717
 
5.1%
E 923739
 
4.4%
L 922583
 
4.4%
Other values (44) 5254770
25.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20824529
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 2762231
13.3%
2620014
12.6%
N 2105500
10.1%
S 1520499
 
7.3%
O 1336993
 
6.4%
T 1181841
 
5.7%
C 1134642
 
5.4%
I 1061717
 
5.1%
E 923739
 
4.4%
L 922583
 
4.4%
Other values (44) 5254770
25.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20824529
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 2762231
13.3%
2620014
12.6%
N 2105500
10.1%
S 1520499
 
7.3%
O 1336993
 
6.4%
T 1181841
 
5.7%
C 1134642
 
5.4%
I 1061717
 
5.1%
E 923739
 
4.4%
L 922583
 
4.4%
Other values (44) 5254770
25.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20824529
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 2762231
13.3%
2620014
12.6%
N 2105500
10.1%
S 1520499
 
7.3%
O 1336993
 
6.4%
T 1181841
 
5.7%
C 1134642
 
5.4%
I 1061717
 
5.1%
E 923739
 
4.4%
L 922583
 
4.4%
Other values (44) 5254770
25.2%
Distinct57
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size43.7 MiB
2025-02-10T11:14:44.799448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length2
Mean length2.0087081
Min length2

Characters and Unicode

Total characters1806158
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowOH
2nd rowIN
3rd rowIN
4th rowOK
5th rowFL
ValueCountFrequency (%)
ca 118116
 
13.1%
nc 79514
 
8.8%
il 65908
 
7.3%
oh 58461
 
6.5%
sd 51095
 
5.7%
tx 47790
 
5.3%
ri 45366
 
5.0%
ny 39592
 
4.4%
va 29002
 
3.2%
de 24537
 
2.7%
Other values (47) 339783
37.8%
2025-02-10T11:14:44.970592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 241398
13.4%
C 229604
12.7%
N 187751
10.4%
I 158854
 
8.8%
O 102604
 
5.7%
L 96914
 
5.4%
D 96078
 
5.3%
T 94941
 
5.3%
M 85034
 
4.7%
S 73385
 
4.1%
Other values (18) 439595
24.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1806158
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 241398
13.4%
C 229604
12.7%
N 187751
10.4%
I 158854
 
8.8%
O 102604
 
5.7%
L 96914
 
5.4%
D 96078
 
5.3%
T 94941
 
5.3%
M 85034
 
4.7%
S 73385
 
4.1%
Other values (18) 439595
24.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1806158
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 241398
13.4%
C 229604
12.7%
N 187751
10.4%
I 158854
 
8.8%
O 102604
 
5.7%
L 96914
 
5.4%
D 96078
 
5.3%
T 94941
 
5.3%
M 85034
 
4.7%
S 73385
 
4.1%
Other values (18) 439595
24.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1806158
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 241398
13.4%
C 229604
12.7%
N 187751
10.4%
I 158854
 
8.8%
O 102604
 
5.7%
L 96914
 
5.4%
D 96078
 
5.3%
T 94941
 
5.3%
M 85034
 
4.7%
S 73385
 
4.1%
Other values (18) 439595
24.3%

NAICS
Real number (ℝ)

Zeros 

Distinct1312
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean398660.95
Minimum0
Maximum928120
Zeros201948
Zeros (%)22.5%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2025-02-10T11:14:45.050259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1235210
median445310
Q3561730
95-th percentile811192
Maximum928120
Range928120
Interquartile range (IQR)326520

Descriptive statistics

Standard deviation263318.31
Coefficient of variation (CV)0.66050691
Kurtosis-1.0476526
Mean398660.95
Median Absolute Deviation (MAD)176300
Skewness-0.26287834
Sum3.5846157 × 1011
Variance6.9336534 × 1010
MonotonicityNot monotonic
2025-02-10T11:14:45.135713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 201948
 
22.5%
722110 27989
 
3.1%
722211 19448
 
2.2%
811111 14585
 
1.6%
621210 14048
 
1.6%
624410 10111
 
1.1%
812112 9230
 
1.0%
561730 8935
 
1.0%
621310 8733
 
1.0%
812320 7894
 
0.9%
Other values (1302) 576243
64.1%
ValueCountFrequency (%)
0 201948
22.5%
111110 32
 
< 0.1%
111120 3
 
< 0.1%
111130 1
 
< 0.1%
111140 94
 
< 0.1%
111150 49
 
< 0.1%
111160 2
 
< 0.1%
111191 3
 
< 0.1%
111199 7
 
< 0.1%
111211 16
 
< 0.1%
ValueCountFrequency (%)
928120 32
< 0.1%
928110 4
 
< 0.1%
927110 1
 
< 0.1%
926150 10
 
< 0.1%
926140 6
 
< 0.1%
926130 3
 
< 0.1%
926120 5
 
< 0.1%
926110 6
 
< 0.1%
925120 1
 
< 0.1%
925110 3
 
< 0.1%

NoEmp
Real number (ℝ)

Skewed 

Distinct599
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.411353
Minimum0
Maximum9999
Zeros6631
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2025-02-10T11:14:45.222684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q310
95-th percentile40
Maximum9999
Range9999
Interquartile range (IQR)8

Descriptive statistics

Standard deviation74.108196
Coefficient of variation (CV)6.4942514
Kurtosis7965.2886
Mean11.411353
Median Absolute Deviation (MAD)3
Skewness80.248244
Sum10260678
Variance5492.0248
MonotonicityNot monotonic
2025-02-10T11:14:45.311106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 154254
17.2%
2 138297
15.4%
3 90674
10.1%
4 73644
 
8.2%
5 60319
 
6.7%
6 45759
 
5.1%
10 31536
 
3.5%
7 31495
 
3.5%
8 31361
 
3.5%
12 20822
 
2.3%
Other values (589) 221003
24.6%
ValueCountFrequency (%)
0 6631
 
0.7%
1 154254
17.2%
2 138297
15.4%
3 90674
10.1%
4 73644
8.2%
5 60319
 
6.7%
6 45759
 
5.1%
7 31495
 
3.5%
8 31361
 
3.5%
9 18131
 
2.0%
ValueCountFrequency (%)
9999 4
< 0.1%
9992 1
 
< 0.1%
9945 1
 
< 0.1%
9090 1
 
< 0.1%
9000 2
 
< 0.1%
8500 1
 
< 0.1%
8041 1
 
< 0.1%
8018 1
 
< 0.1%
8000 7
< 0.1%
7999 1
 
< 0.1%

NewExist
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.6 MiB
1.0
645903 
2.0
253261 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2697492
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 645903
71.8%
2.0 253261
 
28.2%

Length

2025-02-10T11:14:45.384782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-10T11:14:45.423463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 645903
71.8%
2.0 253261
 
28.2%

Most occurring characters

ValueCountFrequency (%)
. 899164
33.3%
0 899164
33.3%
1 645903
23.9%
2 253261
 
9.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2697492
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 899164
33.3%
0 899164
33.3%
1 645903
23.9%
2 253261
 
9.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2697492
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 899164
33.3%
0 899164
33.3%
1 645903
23.9%
2 253261
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2697492
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 899164
33.3%
0 899164
33.3%
1 645903
23.9%
2 253261
 
9.4%

CreateJob
Real number (ℝ)

Skewed  Zeros 

Distinct246
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4303764
Minimum0
Maximum8800
Zeros629248
Zeros (%)70.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2025-02-10T11:14:45.486675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile10
Maximum8800
Range8800
Interquartile range (IQR)1

Descriptive statistics

Standard deviation236.68817
Coefficient of variation (CV)28.075634
Kurtosis1369.911
Mean8.4303764
Median Absolute Deviation (MAD)0
Skewness36.991355
Sum7580291
Variance56021.288
MonotonicityNot monotonic
2025-02-10T11:14:45.572628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 629248
70.0%
1 63174
 
7.0%
2 57831
 
6.4%
3 28806
 
3.2%
4 20511
 
2.3%
5 18691
 
2.1%
10 11602
 
1.3%
6 11009
 
1.2%
8 7378
 
0.8%
7 6374
 
0.7%
Other values (236) 44540
 
5.0%
ValueCountFrequency (%)
0 629248
70.0%
1 63174
 
7.0%
2 57831
 
6.4%
3 28806
 
3.2%
4 20511
 
2.3%
5 18691
 
2.1%
6 11009
 
1.2%
7 6374
 
0.7%
8 7378
 
0.8%
9 3330
 
0.4%
ValueCountFrequency (%)
8800 648
0.1%
5621 1
 
< 0.1%
5199 1
 
< 0.1%
5085 1
 
< 0.1%
3500 1
 
< 0.1%
3100 1
 
< 0.1%
3000 4
 
< 0.1%
2515 1
 
< 0.1%
2140 1
 
< 0.1%
2020 1
 
< 0.1%

RetainedJob
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct358
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.797257
Minimum0
Maximum9500
Zeros440403
Zeros (%)49.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2025-02-10T11:14:45.658078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile20
Maximum9500
Range9500
Interquartile range (IQR)4

Descriptive statistics

Standard deviation237.1206
Coefficient of variation (CV)21.961188
Kurtosis1362.0182
Mean10.797257
Median Absolute Deviation (MAD)1
Skewness36.854812
Sum9708505
Variance56226.179
MonotonicityNot monotonic
2025-02-10T11:14:45.740950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 440403
49.0%
1 88790
 
9.9%
2 76851
 
8.5%
3 49963
 
5.6%
4 39666
 
4.4%
5 32627
 
3.6%
6 23796
 
2.6%
7 16530
 
1.8%
8 15698
 
1.7%
10 15438
 
1.7%
Other values (348) 99402
 
11.1%
ValueCountFrequency (%)
0 440403
49.0%
1 88790
 
9.9%
2 76851
 
8.5%
3 49963
 
5.6%
4 39666
 
4.4%
5 32627
 
3.6%
6 23796
 
2.6%
7 16530
 
1.8%
8 15698
 
1.7%
9 8735
 
1.0%
ValueCountFrequency (%)
9500 1
 
< 0.1%
8800 648
0.1%
7250 1
 
< 0.1%
5000 1
 
< 0.1%
4441 1
 
< 0.1%
4000 2
 
< 0.1%
3900 1
 
< 0.1%
3860 1
 
< 0.1%
3225 1
 
< 0.1%
3200 1
 
< 0.1%

FranchiseCode
Real number (ℝ)

Zeros 

Distinct2768
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2753.7259
Minimum0
Maximum99999
Zeros208835
Zeros (%)23.2%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2025-02-10T11:14:45.826355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile15805
Maximum99999
Range99999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation12758.019
Coefficient of variation (CV)4.6330025
Kurtosis24.409524
Mean2753.7259
Median Absolute Deviation (MAD)0
Skewness4.9752152
Sum2.4760512 × 109
Variance1.6276705 × 108
MonotonicityNot monotonic
2025-02-10T11:14:45.914413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 638554
71.0%
0 208835
 
23.2%
78760 3373
 
0.4%
68020 1921
 
0.2%
50564 1034
 
0.1%
21780 1003
 
0.1%
25650 715
 
0.1%
79140 659
 
0.1%
22470 615
 
0.1%
17998 606
 
0.1%
Other values (2758) 41849
 
4.7%
ValueCountFrequency (%)
0 208835
 
23.2%
1 638554
71.0%
3 12
 
< 0.1%
395 5
 
< 0.1%
399 3
 
< 0.1%
400 2
 
< 0.1%
401 12
 
< 0.1%
404 1
 
< 0.1%
407 34
 
< 0.1%
414 2
 
< 0.1%
ValueCountFrequency (%)
99999 1
 
< 0.1%
92006 4
 
< 0.1%
92000 9
< 0.1%
91999 11
< 0.1%
91450 2
 
< 0.1%
91446 1
 
< 0.1%
91443 2
 
< 0.1%
91435 1
 
< 0.1%
91424 1
 
< 0.1%
91423 2
 
< 0.1%

IsFranchise
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size42.9 MiB
1
690329 
0
208835 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters899164
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 690329
76.8%
0 208835
 
23.2%

Length

2025-02-10T11:14:45.986054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-10T11:14:46.025068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 690329
76.8%
0 208835
 
23.2%

Most occurring characters

ValueCountFrequency (%)
1 690329
76.8%
0 208835
 
23.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 899164
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 690329
76.8%
0 208835
 
23.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 899164
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 690329
76.8%
0 208835
 
23.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 899164
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 690329
76.8%
0 208835
 
23.2%

Term
Real number (ℝ)

High correlation 

Distinct412
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110.77308
Minimum0
Maximum569
Zeros810
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2025-02-10T11:14:46.083924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16
Q160
median84
Q3120
95-th percentile300
Maximum569
Range569
Interquartile range (IQR)60

Descriptive statistics

Standard deviation78.857305
Coefficient of variation (CV)0.7118815
Kurtosis0.18570424
Mean110.77308
Median Absolute Deviation (MAD)33
Skewness1.1209258
Sum99603164
Variance6218.4746
MonotonicityNot monotonic
2025-02-10T11:14:46.168611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
84 230162
25.6%
60 89945
 
10.0%
240 85982
 
9.6%
120 77654
 
8.6%
300 44727
 
5.0%
180 28164
 
3.1%
36 19800
 
2.2%
12 17095
 
1.9%
48 15621
 
1.7%
72 9419
 
1.0%
Other values (402) 280595
31.2%
ValueCountFrequency (%)
0 810
 
0.1%
1 1608
0.2%
2 1809
0.2%
3 2112
0.2%
4 2173
0.2%
5 1866
0.2%
6 3054
0.3%
7 1761
0.2%
8 1693
0.2%
9 1875
0.2%
ValueCountFrequency (%)
569 1
< 0.1%
527 1
< 0.1%
511 1
< 0.1%
505 1
< 0.1%
481 1
< 0.1%
480 1
< 0.1%
461 1
< 0.1%
449 1
< 0.1%
445 1
< 0.1%
443 1
< 0.1%

RevLineCr
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size42.9 MiB
0
899164 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters899164
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 899164
100.0%

Length

2025-02-10T11:14:46.241131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-10T11:14:46.276297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 899164
100.0%

Most occurring characters

ValueCountFrequency (%)
0 899164
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 899164
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 899164
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 899164
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 899164
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 899164
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 899164
100.0%

LowDoc
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size42.9 MiB
0
788829 
1
110335 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters899164
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 788829
87.7%
1 110335
 
12.3%

Length

2025-02-10T11:14:46.319620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-10T11:14:46.358267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 788829
87.7%
1 110335
 
12.3%

Most occurring characters

ValueCountFrequency (%)
0 788829
87.7%
1 110335
 
12.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 899164
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 788829
87.7%
1 110335
 
12.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 899164
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 788829
87.7%
1 110335
 
12.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 899164
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 788829
87.7%
1 110335
 
12.3%

DisbursementGross
Real number (ℝ)

High correlation 

Distinct118859
Distinct (%)13.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201154.02
Minimum0
Maximum11446325
Zeros196
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2025-02-10T11:14:46.729253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10000
Q142000
median100000
Q3238000
95-th percentile761892.5
Maximum11446325
Range11446325
Interquartile range (IQR)196000

Descriptive statistics

Standard deviation287640.85
Coefficient of variation (CV)1.4299533
Kurtosis35.088599
Mean201154.02
Median Absolute Deviation (MAD)70000
Skewness3.9409921
Sum1.8087045 × 1011
Variance8.2737259 × 1010
MonotonicityNot monotonic
2025-02-10T11:14:46.821197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50000 43787
 
4.9%
100000 36714
 
4.1%
25000 27387
 
3.0%
150000 23373
 
2.6%
10000 21328
 
2.4%
35000 14748
 
1.6%
5000 14193
 
1.6%
75000 13528
 
1.5%
20000 13462
 
1.5%
30000 12696
 
1.4%
Other values (118849) 677948
75.4%
ValueCountFrequency (%)
0 196
< 0.1%
1 11
 
< 0.1%
2 3
 
< 0.1%
3 3
 
< 0.1%
4 3
 
< 0.1%
5 2
 
< 0.1%
6 4
 
< 0.1%
7 3
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
11446325 1
< 0.1%
11000000 1
< 0.1%
10465000 1
< 0.1%
9284449 1
< 0.1%
8995000 1
< 0.1%
8607858 1
< 0.1%
8602584 1
< 0.1%
7853275 1
< 0.1%
7699233 1
< 0.1%
7573881 1
< 0.1%
Distinct9859
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
Minimum1961-12-07 00:00:00
Maximum2014-06-25 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-10T11:14:46.904579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:46.993325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

ApprovalFY
Real number (ℝ)

High correlation 

Distinct51
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2001.1436
Minimum1962
Maximum2014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2025-02-10T11:14:47.083443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1962
5-th percentile1991
Q11997
median2002
Q32006
95-th percentile2009
Maximum2014
Range52
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.9138459
Coefficient of variation (CV)0.0029552332
Kurtosis-0.092531047
Mean2001.1436
Median Absolute Deviation (MAD)4
Skewness-0.58537855
Sum1.7993562 × 109
Variance34.973573
MonotonicityNot monotonic
2025-02-10T11:14:47.170216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2005 77525
 
8.6%
2006 76040
 
8.5%
2007 71876
 
8.0%
2004 68290
 
7.6%
2003 58193
 
6.5%
1995 45758
 
5.1%
2002 44391
 
4.9%
1996 40112
 
4.5%
2008 39540
 
4.4%
1997 37748
 
4.2%
Other values (41) 339691
37.8%
ValueCountFrequency (%)
1962 1
 
< 0.1%
1965 1
 
< 0.1%
1966 1
 
< 0.1%
1967 2
 
< 0.1%
1968 2
 
< 0.1%
1969 4
 
< 0.1%
1970 8
 
< 0.1%
1971 20
 
< 0.1%
1972 27
< 0.1%
1973 52
< 0.1%
ValueCountFrequency (%)
2014 268
 
< 0.1%
2013 2458
 
0.3%
2012 5997
 
0.7%
2011 12608
 
1.4%
2010 16848
 
1.9%
2009 19126
 
2.1%
2008 39540
4.4%
2007 71876
8.0%
2006 76040
8.5%
2005 77525
8.6%

GrAppv
Real number (ℝ)

High correlation 

Distinct22128
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean192686.98
Minimum200
Maximum5472000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2025-02-10T11:14:47.254425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum200
5-th percentile10000
Q135000
median90000
Q3225000
95-th percentile750000
Maximum5472000
Range5471800
Interquartile range (IQR)190000

Descriptive statistics

Standard deviation283263.39
Coefficient of variation (CV)1.4700702
Kurtosis21.018882
Mean192686.98
Median Absolute Deviation (MAD)65000
Skewness3.5207901
Sum1.7325719 × 1011
Variance8.0238149 × 1010
MonotonicityNot monotonic
2025-02-10T11:14:47.339566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50000 69394
 
7.7%
25000 51258
 
5.7%
100000 50977
 
5.7%
10000 38366
 
4.3%
150000 27624
 
3.1%
20000 23434
 
2.6%
35000 23181
 
2.6%
30000 21004
 
2.3%
5000 19146
 
2.1%
15000 18472
 
2.1%
Other values (22118) 556308
61.9%
ValueCountFrequency (%)
200 2
 
< 0.1%
300 1
 
< 0.1%
400 2
 
< 0.1%
500 33
 
< 0.1%
700 4
 
< 0.1%
800 4
 
< 0.1%
950 1
 
< 0.1%
1000 444
< 0.1%
1200 12
 
< 0.1%
1300 2
 
< 0.1%
ValueCountFrequency (%)
5472000 1
 
< 0.1%
5000000 40
< 0.1%
4991700 1
 
< 0.1%
4950000 1
 
< 0.1%
4908500 1
 
< 0.1%
4900000 2
 
< 0.1%
4872000 1
 
< 0.1%
4869000 1
 
< 0.1%
4830000 1
 
< 0.1%
4800000 1
 
< 0.1%

SBA_Appv
Real number (ℝ)

High correlation 

Distinct38326
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean149488.79
Minimum100
Maximum5472000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2025-02-10T11:14:47.423123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile5000
Q121250
median61250
Q3175000
95-th percentile626250
Maximum5472000
Range5471900
Interquartile range (IQR)153750

Descriptive statistics

Standard deviation228414.56
Coefficient of variation (CV)1.5279712
Kurtosis25.325514
Mean149488.79
Median Absolute Deviation (MAD)48750
Skewness3.6752753
Sum1.3441494 × 1011
Variance5.2173212 × 1010
MonotonicityNot monotonic
2025-02-10T11:14:47.508624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25000 49579
 
5.5%
12500 40147
 
4.5%
5000 31135
 
3.5%
50000 25047
 
2.8%
10000 17009
 
1.9%
17500 16141
 
1.8%
15000 14490
 
1.6%
7500 12781
 
1.4%
127500 11946
 
1.3%
80000 10965
 
1.2%
Other values (38316) 669924
74.5%
ValueCountFrequency (%)
100 2
 
< 0.1%
150 1
 
< 0.1%
200 2
 
< 0.1%
250 33
 
< 0.1%
350 4
 
< 0.1%
400 4
 
< 0.1%
475 1
 
< 0.1%
500 442
< 0.1%
600 12
 
< 0.1%
650 2
 
< 0.1%
ValueCountFrequency (%)
5472000 1
 
< 0.1%
5000000 1
 
< 0.1%
4869000 1
 
< 0.1%
4582000 1
 
< 0.1%
4500000 23
< 0.1%
4492530 1
 
< 0.1%
4410000 1
 
< 0.1%
4320000 1
 
< 0.1%
4050000 4
 
< 0.1%
4000000 13
< 0.1%

Interactions

2025-02-10T11:14:37.630655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:16.553305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:18.407063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:20.747542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:22.690640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:24.702618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:26.534060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:28.333212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:30.167202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:31.956508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:33.993443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:35.846568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:37.773542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:16.712731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:18.588332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:20.908489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:22.862441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:24.848425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:26.671762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:28.555782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:30.314949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:32.099451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:34.143195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:36.005342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:37.914258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:16.858307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:18.825360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:21.058205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:23.004702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:24.988982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:26.812988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:28.701571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:30.463601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:32.240918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:34.307573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:36.157296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:38.068064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:16.999205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:19.043921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:21.252235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:23.142021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:25.183663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:26.949023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:28.846415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:30.609980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:32.405024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:34.467182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:36.310147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:38.216305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:17.164557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-10T11:14:21.407467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:23.332916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:25.319903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:27.087293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:29.005669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:30.797902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:32.584272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-10T11:14:19.481524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:21.564401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:23.517194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-10T11:14:29.146480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:30.942801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-10T11:14:34.783661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-10T11:14:19.666347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:21.751723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:23.666475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-10T11:14:29.283932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:31.087733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:33.117104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:34.934275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:36.753173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-10T11:14:31.232074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:33.259622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:35.077639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:36.902442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:38.791978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:17.773877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:20.023992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:22.043068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:24.027224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:25.913645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:27.677144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:29.566494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:31.379760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:33.402991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:35.219989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:37.050236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:38.950485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:17.924767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:20.187531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:22.207585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:24.177227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:26.120097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:27.857896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:29.716182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:31.529437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:33.550021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:35.368519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:37.204115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:39.098431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:18.069245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:20.394529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:22.391342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:24.346058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:26.255076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:28.022414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:29.855836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:31.671895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:33.695778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:35.519506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:37.345935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:39.237534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:18.217018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:20.567928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:22.548147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:24.554978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:26.393360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:28.177303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:29.996591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:31.815797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:33.845472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:35.677567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-10T11:14:37.488066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-02-10T11:14:47.583912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ApprovalFYCreateJobDisbursementGrossFranchiseCodeGrAppvIsFranchiseLoanNr_ChkDgtLowDocMIS_StatusNAICSNewExistNoEmpRetainedJobSBA_AppvTermUrbanRuralZip
ApprovalFY1.0000.268-0.222-0.452-0.3000.644-0.2780.3750.3270.4470.054-0.2260.546-0.366-0.2970.659-0.038
CreateJob0.2681.0000.110-0.0540.0930.046-0.0310.0100.0120.1570.0030.0340.3770.0780.0820.0250.026
DisbursementGross-0.2220.1101.0000.2040.9650.0300.1020.0510.031-0.1240.0210.445-0.0700.9360.5210.0400.115
FranchiseCode-0.452-0.0540.2041.0000.2590.1310.3920.0360.022-0.0910.1390.121-0.2630.2850.1960.0130.031
GrAppv-0.3000.0930.9650.2591.0000.0990.1390.1160.074-0.1470.0500.455-0.1380.9860.5580.0510.119
IsFranchise0.6440.0460.0300.1310.0991.0000.4790.2060.2400.2060.0490.0000.0460.0900.2150.2800.093
LoanNr_ChkDgt-0.278-0.0310.1020.3920.1390.4791.0000.2470.237-0.0500.0860.075-0.1420.1690.1210.1890.031
LowDoc0.3750.0100.0510.0360.1160.2060.2471.0000.0840.1540.1610.0030.0100.0970.1690.2130.145
MIS_Status0.3270.0120.0310.0220.0740.2400.2370.0841.0000.1480.0190.0040.0130.0700.4910.2100.080
NAICS0.4470.157-0.124-0.091-0.1470.206-0.0500.1540.1481.0000.132-0.1540.271-0.175-0.0810.432-0.034
NewExist0.0540.0030.0210.1390.0500.0490.0860.1610.0190.1321.0000.0030.0030.0410.1230.0410.123
NoEmp-0.2260.0340.4450.1210.4550.0000.0750.0030.004-0.1540.0031.0000.1240.4490.2000.0100.059
RetainedJob0.5460.377-0.070-0.263-0.1380.046-0.1420.0100.0130.2710.0030.1241.000-0.205-0.1570.025-0.026
SBA_Appv-0.3660.0780.9360.2850.9860.0900.1690.0970.070-0.1750.0410.449-0.2051.0000.5890.0510.131
Term-0.2970.0820.5210.1960.5580.2150.1210.1690.491-0.0810.1230.200-0.1570.5891.0000.2070.142
UrbanRural0.6590.0250.0400.0130.0510.2800.1890.2130.2100.4320.0410.0100.0250.0510.2071.0000.126
Zip-0.0380.0260.1150.0310.1190.0930.0310.1450.080-0.0340.1230.059-0.0260.1310.1420.1261.000

Missing values

2025-02-10T11:14:39.484367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-10T11:14:40.898631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

MIS_StatusLoanNr_ChkDgtStateZipUrbanRuralBankBankStateNAICSNoEmpNewExistCreateJobRetainedJobFranchiseCodeIsFranchiseTermRevLineCrLowDocDisbursementGrossApprovalDateApprovalFYGrAppvSBA_Appv
011000014003IN477110FIFTH THIRD BANKOH45112042.00011840160000.01997-02-28199760000.048000.0
111000024006IN4652601ST SOURCE BANKIN72241022.00011600140000.01997-02-28199740000.032000.0
211000034009IN474010GRANT COUNTY STATE BANKIN62121071.0001118000287000.01997-02-281997287000.0215250.0
311000044001OK7401201ST NATL BK & TR CO OF BROKENOK021.00011600135000.01997-02-28199735000.028000.0
411000054004FL328010FLORIDA BUS. DEVEL CORPFL0141.0771124000229000.01997-02-281997229000.0229000.0
511000084002CT60620TD BANK, NATIONAL ASSOCIATIONDE332721191.0001112000517000.01997-02-281997517000.0387750.0
601000093009NJ70830WELLS FARGO BANK NATL ASSOCSD0452.000004500600000.01980-06-021980600000.0499998.0
711000094005FL344910REGIONS BANKAL81111812.00011840145000.01997-02-28199745000.036000.0
811000104006FL324560CENTENNIAL BANKFL72131022.0001129700305000.01997-02-281997305000.0228750.0
911000124001CT60730WEBSTER BANK NATL ASSOCCT032.00011840170000.01997-02-28199770000.056000.0
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89915819995563001TX750620LOANS FROM OLD CLOSED LENDERSDC052.00011840179000.01997-02-27199779000.063200.0
89915919995573004OH432210JPMORGAN CHASE BANK NATL ASSOCIL45112061.00011600070000.01997-02-27199770000.056000.0
89916019995603000OH432210JPMORGAN CHASE BANK NATL ASSOCIL45113061.00011600085000.01997-02-27199785000.042500.0
89916119995613003CA934550RABOBANK, NATIONAL ASSOCIATIONCA332321261.0001110800300000.01997-02-271997300000.0225000.0
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89916319996003010HI967340CENTRAL PACIFIC BANKHI012.00011480030000.01997-02-27199730000.024000.0